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Geospatial Data Analysis: A Review of Theory and Methods

B. Geospatial data based recommender system for the location of health services

Martnez et al [32] have proposed a geospatial application that focuses on locating health care centers within a certain range of distance is presented. The proposed methodology works in three levels. The first level called the localization level, works towards the user’s location, the second level makes the semantic analysis, which uses an application ontology to describe the conceptualization of the health care services, to which the user can go. The third level generates and displays statistics of the vehicular traffic.

In the localization level, the current location of the patient is determined using a multilateration probabilistic method.

This method considers the distances to the antennas that are in the range of the mobile device and implements a calibration process.

When the system has the antenna positions and the distance between the user and the antenna, a system of equations is solved to obtain the current location of the patient. In the semantic analysis level, the user selects the type of the health insurance and depending on the selection, the health care institutions and the specialties are displayed. According to the distance between the location of the device and the hospitals the markers are colored and the scale of colors is displayed. In the visualization level, the historical data of traffic is analyzed for the coverage area and are displayed on the map. The computation is based on information contained in geospatial database, computing the average of the velocities at earlier dates to be shown under coverage radius determined by the user.

Three different types of queries are supported in this system. The first is ”General”, where the user specifies the coverage radius. The second is ”By Category”, where the user specifies the coverage radius and the type of the health care center. The third is ”By Service”, where the user must select a medical specialty.

C. Seismic hazard assessment

Zoran [33] presents the study of analysis of seismic Vranea zone in Romania, which is at the conjunction of four tectonic blocks and is considered one of the most seismically active areas in Europe. The author has come up with the steps that help in the zonation of the seismic area, which can be thought of as the factors during the data collection process.

Jaishree et al [34] have proposed a methodology for microzonation of the seismic area. Their case study was based on Chennai, TamilNadu(India), since it is in zone 3 after experiencing strong frequent tremors after September 2001. They propose seismic microzonation that can be generated with GIS platform using themes such as Peak Ground Acceleration, Geology, Bed rock depth and lineaments.

Their proposed work involves the following steps:

1. Data collection,

2. Integration of the themes in GIS,

3. Spatial and proximity analysis,

4. Seismic zonation and

5. Datavisualization.

The peak ground acceleration is obtained based on the calculated attenuation relationship. The geology is an important factor for seismic study. Harder the rock the seismic wave velocity will be more and this reduces earthquake intensity in areas having hard rocks. whereas seismic wave velocity will be low in areas having fluvial deposits, so the earthquake hazard is more in those regions.Hence the geology map of the study region is generated. The third map generated is the lineaments map followed by the bedrock depth configuration map. The bedrock configuration of a site will give an idea about the basement topography. which, in turn helps in the study of of frequencies and amplitudes of ground motions. All the generated map layers are overlaid in order to obtain the microzonation map of the study area.

The influence of these layers were identified using hierarchical approach developed by Satty [35]. The importance was given in the order of peak ground acceleration, bedrockdepth, lineaments and geology respectively. These values aid in decision making process in the infrastructure development in the city.


This study reviewed a diverse of theory and methods for geospatial data analysis. Given the unique characteristics of spatial data the following were identified as some of the existing open challenges:

1) Algorithms to handle spatial streaming data
2) New spatial data indexing schemes
3) Volunteered Geographic information systems
4) Mobile mapping and location based services
5) Object based data models for continuous data

The GPS(Global Positioning System) traces of Public Transit Systems which can be considered as spatial stream data are being stored in the huge volumes in their databases. Newer indexing techniques are required to store and retrieve the data back efficiently. These kinds of datasets may include a time series of attributes such as vehicle location, vehicle speed, fuel levels, emissions of greenhouse gases etc.

However, these datasets tend to grow big. Utilizing such datasets require the development of novel algorithms that exploit the modern computing architectures and computational middleware.

More details on the issues that arise in mobility services with respect to GIS could be found on [36].

The geographic information from the social media feeds represents a new type of geographic information. It is referred to as Ambient Geographic Information(AGI) [37] as it is embedded in the content of these feeds often across the content of numerous entries rather than within single one, and has to be extracted somehow. Nevertheless, it is of importance as it communicates real-time information about the emerging issues.

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